




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:						Any					       =  logging.get_logger()

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int      ,				lowercase      :			str      ,				lowercase      :			LevitConfig      ,				lowercase      :			Path      ,				lowercase      :			bool = True	):


      '''simple docstring'''



      print(f"""Converting {name}..."""	)

      with torch.no_grad():
            if hidden_sizes == 1_28:
                  if name[-1] == "S":
                        lowerCamelCase_							=       timm.create_model('levit_128s'      ,				pretrained=lowercase	)
                  else:
                        lowerCamelCase_							=       timm.create_model('levit_128'      ,				pretrained=lowercase	)
            if hidden_sizes == 1_92:
                  lowerCamelCase_							=       timm.create_model('levit_192'      ,				pretrained=lowercase	)
            if hidden_sizes == 2_56:
                  lowerCamelCase_							=       timm.create_model('levit_256'      ,				pretrained=lowercase	)
            if hidden_sizes == 3_84:
                  lowerCamelCase_							=       timm.create_model('levit_384'      ,				pretrained=lowercase	)

            from_model.eval()
            lowerCamelCase_							=       LevitForImageClassificationWithTeacher(lowercase	).eval()
            lowerCamelCase_							=       OrderedDict()

            lowerCamelCase_							=       from_model.state_dict()
            lowerCamelCase_							=       list(from_model.state_dict().keys()	)
            lowerCamelCase_							=       list(our_model.state_dict().keys()	)
            print(len(lowercase	)      ,				len(lowercase	)	)
            for i in range(len(lowercase	)	):
                  lowerCamelCase_							=       weights[og_keys[i]]
            our_model.load_state_dict(lowercase	)

            lowerCamelCase_							=       torch.randn((2, 3, 2_24, 2_24)	)
            lowerCamelCase_							=       from_model(lowercase	)
            lowerCamelCase_							=       our_model(lowercase	).logits

      assert torch.allclose(lowercase      ,				lowercase	), "The model logits don't match the original one."

      lowerCamelCase_							=       name
      print(lowercase	)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name	)
            lowerCamelCase_							=       LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name	)

            print(f"""Pushed {checkpoint_name}"""	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Path      ,				lowercase      :			str = None      ,				lowercase      :			bool = True	):


      '''simple docstring'''



      lowerCamelCase_							=       'imagenet-1k-id2label.json'
      lowerCamelCase_							=       10_00
      lowerCamelCase_							=       (1, num_labels)

      lowerCamelCase_							=       'huggingface/label-files'
      lowerCamelCase_							=       num_labels
      lowerCamelCase_							=       json.load(open(hf_hub_download(lowercase      ,				lowercase      ,				repo_type='dataset'	)      ,				'r'	)	)
      lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}

      lowerCamelCase_							=       idalabel
      lowerCamelCase_							=       {v: k for k, v in idalabel.items()}

      lowerCamelCase_							=       partial(lowercase      ,				num_labels=lowercase      ,				idalabel=lowercase      ,				labelaid=lowercase	)

      lowerCamelCase_							=       {
          'levit-128S': 1_28,
          'levit-128': 1_28,
          'levit-192': 1_92,
          'levit-256': 2_56,
          'levit-384': 3_84,
      }

      lowerCamelCase_							=       {
          'levit-128S': ImageNetPreTrainedConfig(
              hidden_sizes=[1_28, 2_56, 3_84]      ,				num_attention_heads=[4, 6, 8]      ,				depths=[2, 3, 4]      ,				key_dim=[16, 16, 16]      ,				drop_path_rate=0      ,				),
          'levit-128': ImageNetPreTrainedConfig(
              hidden_sizes=[1_28, 2_56, 3_84]      ,				num_attention_heads=[4, 8, 12]      ,				depths=[4, 4, 4]      ,				key_dim=[16, 16, 16]      ,				drop_path_rate=0      ,				),
          'levit-192': ImageNetPreTrainedConfig(
              hidden_sizes=[1_92, 2_88, 3_84]      ,				num_attention_heads=[3, 5, 6]      ,				depths=[4, 4, 4]      ,				key_dim=[32, 32, 32]      ,				drop_path_rate=0      ,				),
          'levit-256': ImageNetPreTrainedConfig(
              hidden_sizes=[2_56, 3_84, 5_12]      ,				num_attention_heads=[4, 6, 8]      ,				depths=[4, 4, 4]      ,				key_dim=[32, 32, 32]      ,				drop_path_rate=0      ,				),
          'levit-384': ImageNetPreTrainedConfig(
              hidden_sizes=[3_84, 5_12, 7_68]      ,				num_attention_heads=[6, 9, 12]      ,				depths=[4, 4, 4]      ,				key_dim=[32, 32, 32]      ,				drop_path_rate=0.1      ,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]      ,				lowercase      ,				names_to_config[model_name]      ,				lowercase      ,				lowercase	)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]      ,				lowercase      ,				lowercase      ,				lowercase      ,				lowercase	)
      return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase							:						Optional[int]					       =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default=None,
     type=str,
     help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 lowerCamelCase							:						Optional[int]					       =  parser.parse_args()
 lowerCamelCase							:						Path					       =  args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )




@require_torch
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    3
   _UpperCamelCase           =    250

   _UpperCamelCase           =    ids_tensor((batch_size, length)							, lowercase_)
   _UpperCamelCase           =    torch.ones((batch_size, length)							, device=lowercase_							, dtype=torch.float) / length
   return input_ids, scores







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    StoppingCriteriaList(
       [
           MaxLengthCriteria(max_length=10),
           MaxTimeCriteria(max_time=0.1),
       ])

   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxLengthCriteria(max_length=10)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    MaxNewTokensCriteria(start_length=5							, max_new_tokens=5)

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(9)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(10)
   self.assertTrue(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    StoppingCriteriaList([criteria])
   self.assertEqual(criteria_list.max_length							, 10)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self._get_tensors(5)

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1)
   self.assertFalse(criteria(lowercase_							, lowercase_))

   _UpperCamelCase           =    MaxTimeCriteria(max_time=0.1							, initial_timestamp=time.time() - 0.2)
   self.assertTrue(criteria(lowercase_							, lowercase_))







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 10)

   with self.assertWarns(lowercase_):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])							, 11)

   _UpperCamelCase           =    validate_stopping_criteria(StoppingCriteriaList()							, 11)

   self.assertEqual(len(lowercase_)							, 1)




